Journal of Advanced Research

Background The global prevalence of autism spectrum disorder (ASD) has significantly increased over the past two decades. Despite substantial research advances, critical aspects, including etiology, diagnostic biomarkers, and pharmacological interventions, remain incompletely elucidated. This persistent knowledge gap warrants systematic mapping of the field's evolution to inform future research priorities. Methods A bibliometric analysis of ASD-related publications indexed in Web of Science was conducted from January 2020 to May 2025. Following a systematic deduplication process, original articles, reviews, case reports, and clinical trials were included in the analysis. The analytical framework comprised co-authorship networks, institutional collaboration patterns, national research contributions, and keyword co-occurrence structures, all of which were examined using CiteSpace (version 5.8.R3) and VOSviewer. Results After deduplication, 8,162 publications (January 2020-May 2025) were analyzed. The annual output grew steadily, confirming ASD as a sustained priority in neuroscience. Research remains academia-driven, led by the United States, with China as the second-largest contributor. Chinese institutions place greater emphasis on mechanistic and developmental phenotyping, which aligns with national priorities. These studies maintain strong methodological rigor, and their growing volume underscores the central role of ASD in translational neuroscience. Conclusion Future research on ASD should focus on strengthening case identification, refining clinical phenotyping, and expanding large-scale cohort studies to advance our understanding of its etiology and identify reliable diagnostic biomarkers. It is equally important to develop and evaluate targeted interventions for core symptoms and integrate telemedicine into service delivery models. A critical yet understudied priority is improving the quality of life for autistic individuals and their families, an area in which research globally, including in China, requires greater depth and consistency. With China's growing investment in autism research, it is well-positioned to contribute to these pressing international challenges.

IntroductionBipolar disorder (BD) is characterized by recurring episodes of mania and depression. Despite extensive research, the pathophysiology underlying these mood swings remains elusive. Emerging evidence indicates a potential role for neuroinflammation and microglial activation in the pathophysiology of BD. MethodsWe employed a reverse-translational approach to generate directly induced microglia-like (iMG) cells from peripheral blood monocytes of a single patient with BD, repeatedly sampled across depressive, manic, and subsequent depressive phases. RNA sequencing was performed on iMG cells at each time point to identify differentially expressed genes related to mood state transitions. ResultsA thorough analysis of longitudinal gene expression data has led to the identification of three functional gene categories: "state-dependent genes", "depression-to-mania transition genes (named: firing genes)", and "mania-to-depression transition genes (named: extinguishing genes)". A total of 168 firing, 59 extinguishing, and 77 state-dependent genes were identified. Notably, functional annotation revealed that, compared to the extinction gene set, the firing gene set was enriched in immune and inflammatory response pathways, particularly early-response cytokines such as IL1B and TNF. ConclusionsBased on these findings, we propose that inflammatory immunomodulation by microglia contributes to mood switching in BD, especially in the process of depression-to-mania transition. The classification of genes by their relationship to state transitions offers a novel framework for understanding the molecular mechanisms underlying this complex disorder and may identify potential therapeutic targets to stabilize mood. Further validation with larger cohorts is warranted.

Background Menstrual cycle disturbances have been increasingly reported after COVID-19 vaccination, raising questions about their prevalence and clinical significance among women of reproductive age. Objective This study aimed to investigate the incidence and types of menstrual cycle alterations following different doses of COVID-19 vaccines among Iranian women of reproductive age. Methods A cross-sectional survey was conducted among vaccinated women who reported their menstrual cycle status before and after each vaccine dose. Data on cycle regularity, flow characteristics, and specific menstrual disorders were collected and analyzed. Results Menstrual cycle alterations were reported by 28.8%, 25.4%, 30.3%, and 68.4% of participants after the first, second, third, and fourth vaccine doses, respectively. The most common changes were oligomenorrhea after the first and second doses (8.9% and 5.6%), menorrhagia after the third dose (5.3%), and hypomenorrhea after the fourth dose (8.3%). Comparisons with international studies revealed a wide variation in prevalence (ranging from 25% to 78%), which may be explained by differences in methodology, population characteristics, vaccine types, and pre-vaccination health status. Conclusion A considerable proportion of Iranian women experienced menstrual alterations following COVID-19 vaccination, most commonly oligomenorrhea, menorrhagia, and hypomenorrhea. While generally self-limiting, these findings highlight the need to integrate menstrual health into post-vaccination monitoring and patient counseling. Future research should explore the underlying immune-endocrine mechanisms and long-term clinical implications of these changes.

Background: Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease characterized by death of upper and lower motor neurons, usually presented with clinical heterogeneity. Fluid biomarker development remains dominated by neurofilament light chain (NEFL), a marker of neuroaxonal injury. NEFL is however unspecific to ALS and its phenotypes and there is currently a lack of biomarkers that capture ALS heterogeneity such as onset site and ALS-frontotemporal spectrum disorder (ALS-FTSD). Therefore, we investigated whether plasma proteomics could reveal pathway-level signatures that stratify and explain ALS heterogeneity. Methods: We profiled ~5,400 plasma proteins (Olink Explore HT) in 299 patients with ALS and 50 age- and sex comparable healthy controls. We used two complementary analytic frameworks: (i) differential protein abundance analysis to identify altered proteins in ALS and across clinical subgroups, and (ii) weighted gene correlation network analysis (WGCNA) to identify coordinated protein modules and relate them to ALS diagnosis and to ALS-specific clinical traits (site of onset, ALS-FTSD, ALS functional rating scale-revised (ALSFRS-R) score, and plasma NEFL). Results: Differential abundance analysis identified 56 proteins altered in ALS versus controls, of which 40 were increased. WGCNA identified 11 co-expression modules, with ALS samples having the strongest correlation to a protein module (n=51) highly enriched for muscle-related proteins. Out of the 40 proteins that had increased expression levels, 29 overlapped with the muscle-enriched protein module, indicating that muscle related proteins are the dominant circulating proteomic signature in ALS. This signal extended to clinical stratification: spinal-onset patients showed a strong positive association with the muscle-module. Further, differential abundance analysis of spinal- versus bulbar-onset ALS identified changes that mapped predominantly to the same module, supporting a molecular signature of onset phenotype. In contrast, cognitive status (ALS-FTSD) mapped to distinct modules enriched for extracellular matrix/cell-adhesion pathways, consistent with a separable biological axis of disease heterogeneity. Although multiple modules correlated with NEFL, trait-specific signatures were not fully explained by neuroaxonal injury. Notably, the muscle-enriched module increased with higher NEFL and lower ALSFRS-R, supporting its interpretation as a severity-linked, muscle-involvement proxy. Conclusions: Large-scale plasma proteomics reveals that heterogeneity in ALS reflects underlying biological structures. We identified a dominant muscle-associated protein network that distinguished ALS patients from controls and correlated with disease onset phenotype and severity, alongside distinct protein networks linked to ALS-FTSD. By integrating differential protein abundance with network-based analysis, we defined pathway-level biomarker signatures that extend beyond NEFL, enabling biologically informed patient stratification and improved therapeutic monitoring.

Preeclampsia (PE), new onset hypertension after 20 weeks of gestation, affects 10% of all pregnancies in the U.S. and it is associated with progesterone deficiency, chronic inflammation, elevated angiotensin II type 1 receptor agonistic autoantibody (AT1-AA) and endothelial dysfunction. Progesterone, through its receptors, stimulates an anti- inflammatory protein called Progesterone Induced Blocking Factor (PIBF) which decreases during various pregnancy disorders. Therefore, this study was designed to test the hypothesis that a progestogen, in the form of 17-hydroxyprogesterone caproate, stimulates PIBF, lowers vasoactive mechanisms which reduces maternal blood pressure in women with early-onset preeclampsia (EOPE). PE women received 17-OHPC (250 mg, I.M.) and blood draws were collected before and after 17-OHPC supplementation. Placentas were collected at the delivery. 17-OHPC prolonged time of delivery beyond 72h on average and maternal blood pressure was significantly decreased in PE+17- OHPC. Progesterone and PIBF levels were reduced in PE group vs. NP group. Importantly, 17-OHPC increased PIBF and decreased vasoactive mechanisms and markers of inflammation. In conclusion, 17-OHPC or progesterone supplementation improves maternal outcomes in response to EOPE without causing further harm to the fetus.

Non-suicidal self-injury (NSSI) is common among adolescents with emotional disorders, yet biological indicators of current NSSI status remain limited. We developed a genome-aware multi-omics modeling framework in 107 adolescents with emotional disorders, including 53 without NSSI and 54 with current NSSI. The model integrated metabolomic, inflammatory, clinical blood and genome-derived features, with polygenic risk score and rare variant burden used as genetic-context variables. The fusion model achieved the strongest classification performance (mean AUC = 0.811) and outperformed single-omics alternatives, indicating that NSSI status was better represented by distributed multi-omics patterns than by a single biomarker layer. Repeated modeling prioritized 42 stable features, many of which were not significant in conventional univariate testing. Group-specific network reconstruction further revealed peripheral reorganization, including convergence of non-NSSI modules into an NSSI-associated module that linked inflammatory recruitment with weaker immune-communication, repair and support-related signals. Exploratory MRI, gut-related and stress-endocrine analyses provided additional biological anchors, while a compact sentinel marker panel translated the full model into clinically readable profiles. These findings support a distributed, genome-aware peripheral state associated with current NSSI and provide a framework for future validation of multi-omics state markers in adolescent emotional disorders.

ABSTRACT Objective Delayed cerebral infarction (DCIn) is a severe complication following aneurysmal subarachnoid hemorrhage (aSAH). Previous studies suggest that glycemic variability is associated with DCIn. However, whether diabetes status modifies the relationship between glycemic traits and DCIn remains unknown. Methods Clinical data were collected from aSAH patients admitted to the First Affiliated Hospital of Shantou University Medical College between January 2015 and April 2025. The collected data included demographic characteristics, clinical variables, and glycemic traits. Glycemic traits included mean blood glucose (GLU-M), standard deviation of blood glucose (GLU-SD), coefficient of variation of blood glucose (GLU-CV), variance of blood glucose (GLU-Var), range of blood glucose (GLU-R), average real variability of blood glucose (GLU-ARV), and variability independent of the mean (GLU-VIM). After 1:2 case-control matching, conditional logistic regression models were used to evaluate the associations between glycemic traits and DCIn risk, with stratified analyses performed according to diabetes status. Multiplicative interaction terms were additionally included to assess the potential modifying effect of diabetes status. Results A total of 306 patients with aSAH were included. Among them, 102 developed DCIn cases. For each of these 102 cases, two controls were matched by age ({+/-}5 years), sex and year of admission ({+/-}5 years). In the overall population, higher GLU-M and GLU-ARV were associated with increased DCIn risk, with odds ratios (ORs) per 1-SD increase of 1.62 (95% CI, 1.25-2.11) and 1.63 (95% CI, 1.25-2.11), respectively. Among patients without diabetes (n=266), the associations with DCIn per 1-SD were observed for GLU-M (OR, 2.23; 95% CI, 1.56-3.19), GLU-SD (OR, 1.53; 95% CI, 1.13-2.06), GLU-Var (OR, 1.48; 95% CI, 1.04-2.10), and GLU-ARV (OR, 1.88; 95% CI, 1.38-2.55). No significant associations were observed among patients with diabetes. Significant interactions were observed between diabetes status and GLU-SD and GLU-Var, with P for interaction values of 0.033 and 0.032, respectively. Conclusion Higher mean blood glucose and greater glycemic variability are associated with an increased risk of DCIn in aSAH patients, especially in those without diabetes.

Insulin signalling contributes to neurodevelopment and brain function, and insulin resistance (IR)-related traits are associated with cognitive performance. However, the genetic architecture shared across specific cognitive domains and IR-related phenotypes remains insufficiently defined. We analysed large-scale genome-wide association study summary statistics for 11 IR-related traits (N=53,334-933,970) and 10 cognitive measures (N=28,156-436,853) to quantify global and local genetic correlations, fine-map shared association signals, and annotate implicated genes and drug-gene interactions. Pairwise global and local genetic correlations were estimated, and shared high-confidence variants were prioritised using the multivariate Sum of Single Effects model. Positional and expression quantitative trait locus mapping was performed, and implicated genes were examined through functional annotation, tissue enrichment, and drug-gene interaction analyses. Low-to-moderate genetic correlations were observed between six IR-related traits and seven cognitive measures (|rg|=0.08-0.34), with predominantly opposite directions, except for correlations involving visual declarative short-term memory. Local genetic correlations showed mixed effect directions across most trait pairs, and multivariate fine-mapping prioritised 696 shared likely causal variants with high posterior support. Gene annotation indicated enrichment in several pathways, including immune-related, signal transduction, neurogenesis, neurotransmitter metabolism, receptor regulation, and lipid and cholesterol metabolism regulation. Implicated genes were expressed across various brain regions and showed prior associations with neuropsychiatric and cardiometabolic conditions. Several drug-gene interactions were identified, involving immunomodulatory and anti-inflammatory compounds. These findings indicate widespread heterogeneous genetic overlap between IR-related traits, particularly body mass index and waist-to-hip ratio, and cognitive measures of general intelligence, processing speed, and short-term visual declarative memory. The findings prioritise apolipoprotein-related lipid transport and inflammatory and oxidative stress pathways as candidate mechanisms linking cognitive, cardiometabolic, and neuropsychiatric phenotypes.

Air pollution exposure has been associated with increased risk of developing mental health problems. It is possible that individuals at high genetic risk for psychopathology may be more vulnerable to these effects; however, this question remains to be investigated. We leveraged longitudinal data from n=10,620 participants from the Adolescent Brain Cognitive Development Study to first investigate sex-stratified associations of particulate matter (PM2.) exposure and genetic risk with mental health trajectories across 9-16 years including internalizing symptoms and psychotic like experiences (PLEs). Additionally, we tested whether genetic risk for schizophrenia (PRS-SCZ) and major depressive disorder (PRS-MDD) exacerbate the association with PM2. exposure and change in symptoms over time. PM2. exposure was associated with lower decreases in PLEs over time in females (p-FDR=0.005), with no effects on internalising symptom trajectories in either sex. Genetic influences were sex-specific, with higher PRS-SCZ and PRS-MDD linked to greater increases in internalising symptoms in females (p-FDR=0.009; p-FDR=0.022) and higher PRS-MDD associated with greater decreases in PLEs in males (p-FDR=0.001). In females we also observed an interaction between PM2. and PRS-MDD on PLEs trajectories (p-FDR=0.048) such that those with high genetic risk and high PM2.5 exposure demonstrated increases in PLEs over time. Our results suggest that PM2. exposure and polygenic risk for depression jointly shape mental health during adolescence. This underscores the potential of interventions aimed at lowering air pollution during sensitive periods of neurodevelopment in improving adolescent mental health.

Background: Emotion dysregulation is a core feature of bipolar disorder (BD), yet its behavioral expression during depressive episodes, and potential differences between its types, BD-I and BD-II, remain unclear. This study used automated facial-expression analysis during naturalistic affective film viewing to examine subtype-specific and context-dependent emotional responding in bipolar depression. Methods: The sample included 135 participants: 69 healthy controls and 66 patients with BD (BD-I, 23; BD-II, 43). Participants viewed nine emotionally evocative film clips spanning negative, positive, neutral, and socially threatening contexts, while their facial expressions were continuously recorded and quantified using computer vision-based facial-expression analysis. Results: Patients with BD-I showed a distinct, context-dependent facial-expression profile, characterized by greater negative responses across multiple contexts than other groups. Specifically, they showed increased sadness during sad, reward, and amusing clips, and elevated anger during sad and neutral clips. In socially threatening contexts, BD-I participants showed a multivalent pattern of elevated anger, fear, and joy, suggesting poorly coordinated or context-incongruent affective expression. In contrast, BD-II participants did not differ significantly from healthy controls on any emotion, despite depressive symptom severity comparable to BD-I participants. Conclusions: These findings suggest that facial-expression patterns in bipolar depression differ across subtypes. BD-I may be characterized by heightened negative reactivity and altered context-appropriate modulation of emotional expression, whereas BD-II may not show comparable alterations in overt facial output. Automated facial-expression analysis during naturalistic stimulation may provide a useful behavioral marker for characterizing subtype-specific affective disturbance in bipolar depression and related psychopathology.

Background: Prospective pregnancy registries and biobanking infrastructures are essential for future translational studies investigating maternal, placental and offspring health. However, circulating nucleic acid analyses are highly sensitive to preanalytical variability, particularly regarding blood-collection tube type and sample processing conditions. We established a prospective pregnancy registry and biobanking workflow at Cruces University Hospital and evaluated the impact of preanalytical variables on circulating cell-free DNA (cfDNA) and cell-free RNA (cfRNA) preservation in maternal plasma collected at delivery. Methods: The Registry of Pregnant Women at Cruces University Hospital was designed as a prospective infrastructure integrating placental sampling, maternal blood collection and ethically controlled future access to maternal and offspring clinical data. Within this framework, peripheral blood samples from 50 women at delivery were simultaneously collected into EDTA, Norgen and Roche tubes. Plasma samples processed within or after 24 hours following collection underwent cfDNA/cfRNA extraction, electrophoretic profiling, fluorometric quantification and RT-qPCR analyses targeting different stress-related genes. Results: By the end of June 2026, 1,127 women had been prospectively recruited into the registry, with 661 plasma samples, 637 serum samples and 858 sets of four placental biopsies collected, processed and stored in the Basque Biobank. In the preanalytical substudy, EDTA tubes yielded higher cfDNA concentrations, likely reflecting reduced cellular preservation and genomic DNA contamination. In contrast, Roche tubes showed superior cfRNA preservation, with higher cfRNA concentrations and more consistent detection of the characteristic 5S rRNA peak compared with EDTA and Norgen tubes. Processing delays beyond 24 hours reduced cfRNA concentration, while associations between circulating transcripts and gestational age were more consistently detectable in preservative-containing tubes. Conclusions: Prospective infrastructures like ours offer strong foundation for large scale, long-term studies in the framework of the Developmental Origins of Health and Disease hypothesis. Technically, Roche tubes provided superior cfRNA preservation and enhanced sensitivity for detecting subtle biological associations, supporting the importance of standardized preanalytical workflows within prospective pregnancy biobanking resource.

Low response rates to immune checkpoint inhibitors (ICIs) in solid tumors are often driven by insufficient tumor-infiltrating CD8 T cells and immunosuppressive tumor microenvironment (TME). Although interleukin-2 (IL-2) potently expands and activates CD8 T cells, its clinical use is limited by rapid clearance, dose-limiting toxicity, and regulatory T cell (Treg) stimulation. Engineered IL-2 variants have not yet achieved meaningful clinical efficacy. Here, polymer-modified mesoporous silica nanoparticles displaying dense, unmodified wild-type IL-2 on their surface (IL2-NP) are developed, conferring proteolytic stability and tumor retention. IL2-NP enables avidity-mediated CD8 T cell binding and enhances proliferation and effector function without increased Treg binding or proliferation. Intratumoral IL2-NP expands CD8 T cells, increases CD8/Treg ratios, and reprograms TME through dendritic cell activation and M1-like macrophage polarization. IL2-NP induces regression of both treated and untreated distant colorectal tumors in a CD8 T cell-dependent manner. IL2-NP synergizes with ICIs and leads to complete tumor regression and immunological memory that protect against rechallenge. Treatment is well tolerated, with strong efficacy also observed in triple-negative breast and metastatic ovarian cancer models. Overall, intratumoral IL2-NP elicits robust systemic antitumor immunity, offering a promising strategy to enhance ICIs, cancer vaccines, and adoptive T-cell therapies. Graphical abstractThis work introduces a nanoparticle platform that overcomes major shortcomings of IL-2 immunotherapy by presenting wild-type IL-2 at high density on the nanoparticle surface, thereby increasing binding avidity to effector T cells. The resulting IL-2 nanoparticles enhance cytotoxic T cell expansion, reprogram the tumor microenvironment, and augment responses to immune checkpoint blockade to achieve robust ant-tumor immune response in mouse tumor models. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=182 SRC="FIGDIR/small/738558v1_ufig1.gif" ALT="Figure 1"> View larger version (82K): org.highwire.dtl.DTLVardef@12f8c8corg.highwire.dtl.DTLVardef@b46b1forg.highwire.dtl.DTLVardef@e4efc5org.highwire.dtl.DTLVardef@3993e6_HPS_FORMAT_FIGEXP M_FIG C_FIG

Background: Anemia during pregnancy remains a major public health concern, particularly in low- and middle-income countries, where it contributes substantially to maternal and neonatal morbidity and mortality. Identifying women at increased risk is essential for timely intervention and improved pregnancy outcomes. Objective: This study aimed to identify the significant predictors of anemia among pregnant women using logistic regression and to evaluate the association between selected clinical and sociodemographic characteristics and anemia. Methods: A cross-sectional study was conducted using secondary data obtained from a community maternity care facility in the Suame Municipality of the Ashanti Region, Ghana. Pregnant women who attended antenatal care during the study period and had complete information on hemoglobin concentration and relevant predictor variables were included. Women with missing hemoglobin measurements at registration or delivery were excluded. Logistic regression analysis was performed to identify independent predictors of anemia. Additional analyses examined the effects of age and weight, as well as the relationship between sickle cell status and blood group. Results: Logistic regression identified diastolic blood pressure, height, hemoglobin concentration at registration, maternal weight and gestational age as significant predictors of anemia during pregnancy (p < 0.05). Although employment status was statistically significant in the model, its direct association with anemia was relatively weak. Maternal age was not significantly associated with anemia. Pregnant women with sickle cell disease had a significantly higher likelihood of anemia. Blood group did not demonstrate a significant relationship with anemia. Effect sizes and confidence intervals were not available in the dataset. Conclusion: Diastolic blood pressure, height, hemoglobin concentration at registration, maternal weight, gestational age, sickle cell status and employment status were identified as important predictors of anemia during pregnancy. These findings highlight the importance of incorporating both clinical and sociodemographic characteristics into antenatal risk assessment and screening programs. Further prospective studies with larger sample sizes and more comprehensive clinical measurements are recommended to validate these findings and strengthen predictive models for anemia during pregnancy.

RNA-binding protein LARP4 plays an important role in T cell activation and differentiation, but its role in B cell biology and the pathogenesis of systemic lupus erythematosus (SLE) remains unclear. This study found that LARP4 was specifically highly expressed in B cells of SLE patients and was positively correlated with disease activity. By constructing T cell-specific and B cell-specific conditional knockout mice, we found that deletion of LARP4 in B cells, but not in T cells, significantly alleviated pristane-induced and Bm12-induced lupus nephritis. Further analysis showed that LARP4 deletion selectively inhibited B cell differentiation into plasma cells, but did not affect germinal center B cell formation. Integrated transcriptomic and metabolomics analyses revealed that this effect is due to reduced phosphatidic acid synthesis and decreased mTORC1 activity caused by mitochondrial oxidative phosphorylation dysfunction. Furthermore, we used LIPEP, a LARP4 inhibitory peptide that effectively mimicked the therapeutic effects of LARP4 gene knockout in the MRL/lpr spontaneous lupus model and outperformed cyclophosphamide in reducing glomerular immune complex deposition and improving extrarenal dermatitis. These results indicates that LARP4 is a key metabolic checkpoint regulating B cell differentiation into Plasma cells and suggest that it may be a potential therapeutic target for SLE.

In Mongolia, an average of 65,000 women become pregnant each year, and about 59,500 babies are born. Although the number of pregnancies is decreasing by 8-12 percent each year, the level of fetal monitor usage remains high. The capital's maternity hospital currently has 27 fetal monitors in use, and an average of 30-35 calls are recorded per month. However, there is a lack of research on the use of fetal monitors, the causes and influencing factors of damage, and the organization of technical services. Therefore, this topic was chosen to determine the usage status of fetal monitors, the causes of malfunctions, and ways to improve them. Purpose To study the causes and factors affecting possible damage and injury during the use of fetal monitors, and to identify ways to reduce them. Materials and methods A one-time study was conducted on 10 MT-610 fetal monitors that were put into operation in 2019 at the Urgo Maternity Hospital in the capital. Data were collected and processed using document analysis methods from the technical passports and call logs of these devices. The factors contributing to common failures were identified using focus group interviews with the engineers and technicians responsible for the equipment. Results This study found that fetal monitor failures are caused by improper use, lack of regular calibration, electrical fluctuations, ambient temperature and humidity, and insufficient medical staff skills, training, and knowledge of how to use the device, all of which contribute to failures and measurement errors. It is also observed that when a replacement part is needed for a monitor that frequently breaks, the monitor is more likely to break again if it is used as a replacement from a previously broken monitor. Therefore, training doctors and nurses who replace spare parts on their use has been observed to significantly reduce future breakdowns. Conclusion According to the study results, the breakdowns and failures of fetal monitoring devices are mainly related to internal system failures, unstable power supply, and wear and tear of accessories and mechanical parts. The highest percentage of device failures indicates the need for special attention to the reliability of the device's basic functions. Additionally, the high percentage of accessory and printer failures indicates the need for proper use and monitoring of the entire device. In addition to technical factors, human misuse, lack of maintenance, and environmental influences also play a significant role in damage. Therefore, it is concluded that to ensure the reliable operation of fetal monitors, it is necessary to perform regular maintenance, stabilize the power supply, improve the quality of accessories, and increase the knowledge and skills of medical staff. Keywords: Fetal Monitoring, Equipment Failure, Risk Factors

The increased vulnerability to mental health problems in adolescence is frequently reported but poorly understood, hampered by a rigid diagnostic system which fails to capture intertwining symptoms and only loosely aligns with biological axes of variability. Here, we reconceptualised the mental health symptoms of young adolescents in the ABCD cohort (N=11862) as a latent topology of overlapping symptom dimensions, using an unsupervised machine learning algorithm to establish how transdiagnostic dimensions co-occur and overlap within individuals. Combining this with a novel classification approach, we delineated zones within this landscape, within which specific profiles of symptoms were robustly represented. These data-driven profiles were leveraged to establish associated resting-state functional connectivity and genetic characteristics. In doing so we recaptured the commonly reported p-factor axis as well as further symptom-subtype dimensions. Gene ontology analysis revealed that shared neurobiological and cellular mechanisms embedded in both the genome and transcriptome may confer risk for psychopathology.

Comprehensive biomarkers of multiple sclerosis (MS) capable of simultaneously diagnosing the condition, capturing symptom severity and predicting treatment efficacy remain elusive. Although several studies have highlighted the pivotal role played by demyelinating lesions in determining MS structural pathology, their relationship with symptom severity is limited. Here, we combined personalized computational brain modeling with magnetoencephalography (MEG) recordings from 17 MS patients and 20 healthy controls (CTR) to derive personalized brain network excitability parameters, which we tested as MS biomarkers. Personalized parameters discriminated between CTR and MS participants with high accuracy, also classifying between progressing and remitting MS patients. Notably, they also predicted MS clinical scales across multiple domains. In all clinical tasks, personalized parameters consistently outperformed standard clinical measures and total lesion loads. Together, these results highlight the potential of personalized brain modelling in deriving integrative MS biomarkers, capable of simultaneously identifying the condition, classifying MS subtypes and predicting symptom severity. d brain modelling in deriving integrative MS biomarkers, capable of simultaneously identifying the condition, classifying between MS subtypes and predicting the severity of symptomatology.

The emotional content of suicide notes is typically examined using categorical coding, where each labeled passage is treated in isolation from its surrounding language. In contrast, dimensional models of psychopathology propose that affective content varies along continuous gradients. We evaluated this proposition directly. Excerpts from 884 annotated suicide notes were embedded in a semantic space defined solely by their linguistic properties, and we investigated whether human-assigned emotion labels changed smoothly across this space. They did: affective tone showed clear spatial autocorrelation (Moran's $I = 0.18$, $z = 19.68$, $p < 0.001$), an effect that replicated across three different encoders and remained after removing all within-note dependencies. Emotions occupied recognizable yet overlapping regions rather than forming distinct clusters and varied substantially in how tightly they were concentrated: love and hopelessness appeared with similar frequency, but love was far more localized ($z = 15.7$ versus $10.8$). Among all emotions, hopelessness was the most linguistically diffuse, implying that a single categorical label is capturing multiple, qualitatively different manifestations of suicidal distress.

PurposeExtracellular vesicles (EVs) are increasingly explored as natural vehicles for drug delivery and gene therapy approaches. However, reproducible yield and scalability of EV production still pose major challenges in the clinical translation of EV-based therapies. In this study, we sought to quantify and characterize EVs released by suspension-cultured HEK293 cells (Expi293F cells) grown in shaker flasks or small-scale bioreactors, to investigate how the culturing environment affects EV production yield. MethodsExpi293F cells were cultivated (N=3) in either shaker flasks or a bioreactor system, and total cell density, viability, and size were monitored. Supernatants were drawn daily post-cell seeding and were analyzed for EV quantity, size, morphology, and CD63 expression. ResultsNo significant differences were observed in terms of total cell density, viability, and cell size between both cultivation settings. However, cultivation of Expi293F cells in the bioreactor environment significantly increased EV yield by 3-fold compared to shaker flask cultivation (p < 0.01). Other parameters such as average nanoparticle size, EV morphology, and CD63 expression remained comparable between both cultivation methods. ConclusionThese results demonstrate that Expi293F-derived EV yield can be increased by culturing cells in a scalable bioreactor system. These findings pave the way towards the production of therapeutic-based EVs in a scalable and reproducible manner suitable for future (pre-)clinical applications.

Study Objectives Co morbid insomnia and sleep apnea (COMISA) is challenging clinically and difficult to treat. Our goal was to assess how much COMISA is the mere addition of two phenotypes or display features indicative of genuine statistical interactions. Methods A total of 152,487 patients from 240 sleep centers across 30 US states were included. Insomnia was defined as difficulty initiating/maintaining sleep with daytime fatigue/sleepiness occurring "often"/"always". OSA was defined as having an Apnea Hypopnea Index (AHI) more than 15 events/h. Modified Poisson regression was conducted to evaluate multiplicative interactions between insomnia and OSA on common comorbidities and sleep symptoms. Additive interactions were also examined. Linear regression models were used to evaluate additive interactions for PSG parameters. The false discovery rate was controlled using the Benjamini Hochberg procedure. Results After adjustment for confounders, insomnia and OSA demonstrated positive interactions for depression, chronic muscular pain, headache, subjective excessive daytime sleepiness (EDS), naps, and pre-sleep anxious and muscular tension (adjusted p < 0.05). Furthermore, insomnia and OSA demonstrated positive interactions for parameters related to respiratory disturbance, including AHI, oxygen desaturation index (ODI), respiratory disturbance index (RDI), total arousal index (AI) and respiratory AI, and negative interactions for minimum oxygen saturation and percentage of rapid eye movement stage (REM%) (adjusted p < 0.05). Furthermore, the adverse effects of insomnia and OSA on AHI, ODI, RDI and REM% were substantially amplified in males. Conclusions Our findings demonstrate that insomnia and OSA do not merely coexist but genuinely interact synergistically to amplify selected adverse clinical outcomes.